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Bayesian VARs: Specification Choices and Forecast Accuracy
Author(s) -
Carriero Andrea,
Clark Todd E.,
Marcellino Massimiliano
Publication year - 2013
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.2315
Subject(s) - robustness (evolution) , econometrics , iterated function , bayesian probability , specification , bayesian vector autoregression , computer science , benchmark (surveying) , range (aeronautics) , variable (mathematics) , point estimation , vector autoregression , economics , statistics , mathematics , artificial intelligence , mathematical analysis , biochemistry , chemistry , materials science , geodesy , composite material , gene , geography
Summary In this paper we discuss how the point and density forecasting performance of Bayesian vector autoregressions (BVARs) is affected by a number of specification choices. We adopt as a benchmark a common specification in the literature, a BVAR with variables entering in levels and a prior modeled along the lines of Sims and Zha ( International Economic Review 1998; 39 : 949–968). We then consider optimal choice of the tightness, of the lag length and of both; evaluate the relative merits of modeling in levels or growth rates; compare alternative approaches to h ‐step‐ahead forecasting (direct, iterated and pseudo‐iterated); discuss the treatment of the error variance and of cross‐variable shrinkage; and assess rolling versus recursive estimation. Finally, we analyze the robustness of the results to the VAR size and composition (using also data for France, Canada and the UK, while the main analysis is for the USA). We obtain a large set of empirical results, but the overall message is that we find very small losses (and sometimes even gains) from the adoption of specification choices that make BVAR modeling quick and easy, in particular for point forecasting. This finding could therefore further enhance the diffusion of the BVAR as an econometric tool for a vast range of applications. Copyright © 2013 John Wiley & Sons, Ltd.